Tracking expertise profiles in community-driven and evolving knowledge curation platforms

Abstract

Acquiring and managing expertise profiles represents a major challenge in any organization, as often, the successful completion of a task depends on finding the most appropriate individual to perform it. User profiling has been extensively utilised as a basis for recommendation, personalisation and matchmaking systems. Accurate user profile generators can improve interaction and collaboration between researchers working in similar domains but in different locations or organizations. They can also assist with identifying the optimum set of researchers with complementary skills for cross-disciplinary research teams at a given time. The topic of expertise modelling has been the subject of extensive research in two main disciplines: Information Retrieval (IR) and Social Network Analysis (SNA). Traditional IR and SNA expertise profiling techniques rely on large corpora of static documents authored by an expert, such as publications, reports or grants, the content of which remains unchanged due to the static and final nature of such resources.Consequently, such techniques build the expertise model through a document-centric approach that provides only a macro-perspective of the knowledge emerging from such documents.With the emergence of Web 2.0, there has been a significant increase in online collaboration, giving rise to vast amounts of accessible and searchable knowledge in platforms where content evolves through individuals' contributions. This increase in participation provides vast sources of information, from which knowledge and intelligence can be derived for modelling the expertise of contributors. However, with the proliferation of collaboration platforms, there has been a significant shift from static to evolving documents. Wikis or collaborative knowledge bases, predominantly in the biomedical domain, support this shift by enabling authors to incrementally and collaboratively refine the content of the embedded documents to reflect the latest advances in knowledge in the field. Regardless of the domain, the content of these living documents changes via microcontributions made by individuals, thus making the macro-perspective, provided by the document as a whole, no longer adequate for capturing the evolution of knowledge or expertise. Hence, expertise profiling is presented with major challenges in the context of dynamic and evolving knowledge. Thus, the shift from static documents to living documents requires a shift in the way in which expertise profiling is performed.This thesis examines methods for advancing the state of the art in expertise modelling by considering dynamic content; i.e., platforms in which, knowledge evolves through microcontributions. Towards this goal, a novel expertise profiling framework is introduced that provides solutions for expertise modelling in the context of platforms where knowledge is subject to continuous evolution through experts' micro-contributions; i.e., given a series of microiii contributions, the aim is to build an expertise profile for the author of those...

Semantic and Time-Dependent Expertise Profiling Models in Community-Driven Knowledge Curation Platforms

“…Regardless of the platform, we are interested in capturing the fine-grained provenance of these micro-contributions including the actions that lead to their creation, as well as the macro-context that hosts these contributions; i.e., paragraph or section of the document in which they appear. Therefore, in the initial phase of our study, we created the Fine-grained Provenance Ontology [17], which combines coarse and fine-grained provenance modeling to capture micro-contributions and their localization in the context of their host living documents. The objective has been to reuse and extend existing, established vocabularies from the Semantic Web that have attracted a considerable user community or are derived from de facto standards.…”